Parnian Afshar
Orcid: 0000-0002-8440-9836
According to our database1,
Parnian Afshar
authored at least 32 papers
between 2016 and 2023.
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Bibliography
2023
Improving the Accuracy of Beauty Product Recommendations by Assessing Face Illumination Quality.
CoRR, 2023
Spatio-Temporal Hybrid Fusion of CAE and SWin Transformers for Lung Cancer Malignancy Prediction.
Proceedings of the IEEE International Conference on Acoustics, 2023
2022
CoRR, 2022
2021
Diagnosis/Prognosis of COVID-19 Chest Images via Machine Learning and Hypersignal Processing: Challenges, opportunities, and applications.
IEEE Signal Process. Mag., 2021
MIXCAPS: A capsule network-based mixture of experts for lung nodule malignancy prediction.
Pattern Recognit., 2021
COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans.
Frontiers Artif. Intell., 2021
CAE-Transformer: Transformer-based Model to Predict Invasiveness of Lung Adenocarcinoma Subsolid Nodules from Non-thin Section 3D CT Scans.
CoRR, 2021
Robust Automated Framework for COVID-19 Disease Identification from a Multicenter Dataset of Chest CT Scans.
CoRR, 2021
COVID-Rate: An Automated Framework for Segmentation of COVID-19 Lesions from Chest CT Scans.
CoRR, 2021
Human-level COVID-19 Diagnosis from Low-dose CT Scans Using a Two-stage Time-distributed Capsule Network.
CoRR, 2021
Hybrid Deep Learning Model For Diagnosis Of Covid-19 Using Ct Scans And Clinical/Demographic Data.
Proceedings of the 2021 IEEE International Conference on Image Processing, 2021
Ct-Caps: Feature Extraction-Based Automated Framework for Covid-19 Disease Identification From Chest Ct Scans Using Capsule Networks.
Proceedings of the IEEE International Conference on Acoustics, 2021
2020
IEEE Signal Process. Lett., 2020
COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images.
Pattern Recognit. Lett., 2020
CoRR, 2020
COVID-CT-MD: COVID-19 Computed Tomography (CT) Scan Dataset Applicable in Machine Learning and Deep Learning.
CoRR, 2020
MDR-SURV: A Multi-Scale Deep Learning-Based Radiomics for Survival Prediction in Pulmonary Malignancies.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020
Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020
2019
Lung Cancer Radiomics: Highlights from the IEEE Video and Image Processing Cup 2018 Student Competition [SP Competitions].
IEEE Signal Process. Mag., 2019
From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and opportunities.
IEEE Signal Process. Mag., 2019
Capsule Networks' Interpretability for Brain Tumor Classification Via Radiomics Analyses.
Proceedings of the 2019 IEEE International Conference on Image Processing, 2019
Capsule Networks for Brain Tumor Classification Based on MRI Images and Coarse Tumor Boundaries.
Proceedings of the IEEE International Conference on Acoustics, 2019
2018
A hierarchical stochastic modelling approach for reconstructing lung tumour geometry from 2D CT images.
J. Exp. Theor. Artif. Intell., 2018
Capsule Networks for Brain Tumor Classification based on MRI Images and Course Tumor Boundaries.
CoRR, 2018
From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities.
CoRR, 2018
CARISI: Convolutional Autoencoder-Based Inter-Slice Interpolation of Brain Tumor Volumetric Images.
Proceedings of the 2018 IEEE International Conference on Image Processing, 2018
Proceedings of the 2018 IEEE International Conference on Image Processing, 2018
Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing, 2018
2016
Intelligent breast cancer recognition using particle swarm optimization and support vector machines.
J. Exp. Theor. Artif. Intell., 2016
Proceedings of the Social Robotics - 8th International Conference, 2016
Proceedings of the 2016 IEEE International Conference on Fuzzy Systems, 2016